NDLI: Detecting and approximating fault lines from randomly scattered data

Content Provider	Springer Nature Link
Author	Crampton, Andrew Mason, John C.
Copyright Year	2005
Abstract	Discretely defined surfaces that exhibit vertical displacements across unknown fault lines can be difficult to approximate accurately unless a representation of the faults is known. Accurate representations of these faults enable the construction of constrained approximation models that can successfully overcome common problems such as over-smoothing. In this paper we review an existing method for detecting fault lines and present a new detection approach based on data triangulations and discrete Gaussian curvature (DGC). Furthermore, we show that if the fault line can be described non-parametrically, then accurate support vector machine (SVM) models can be constructed that are independent of the type of triangulation used in the detection algorithms. We shall also see that SVM models are particularly effective when the data produced by the detection algorithms are noisy. We compare the performances of the various new and established models.
Starting Page	115
Ending Page	130
Page Count	16
File Format	PDF
ISSN	10171398
Journal	Numerical Algorithms
Volume Number	39
Issue Number	1-3
e-ISSN	15729265
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2005-01-01
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	fault lines detection and approximation algorithms discrete Gaussian curvature (DGC) support vector machines (SVMs) Theory of Computation Numeric Computing Mathematics Algebra Algorithms
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics

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